What is loss in gradient descent?
Gradient descent is an iterative optimization algorithm used in machine learning to minimize a loss function. The loss function describes how well the model will perform given the current set of parameters (weights and biases), and gradient descent is used to find the best set of parameters.
Is stochastic gradient descent faster than Minibatch?
SGD converges faster for larger datasets. But, since in SGD we use only one example at a time, we cannot implement the vectorized implementation on it. This can slow down the computations. To tackle this problem, a mixture of Batch Gradient Descent and SGD is used.
Why do we use a mini batch in stochastic gradient descent?
We use a batch of a fixed number of training examples which is less than the actual dataset and call it a mini-batch. Doing this helps us achieve the advantages of both the former variants we saw. So, after creating the mini-batches of fixed size, we do the following steps in one epoch:
What do you need to know about gradient descent?
What gradient descent is and how it works from a high level. What batch, stochastic, and mini-batch gradient descent are and the benefits and limitations of each method. That mini-batch gradient descent is the go-to method and how to configure it on your applications.
How is mini-batch gradient descent used in deep learning?
Mini-batch gradient descent seeks to find a balance between the robustness of stochastic gradient descent and the efficiency of batch gradient descent. It is the most common implementation of gradient descent used in the field of deep learning.
Is the gradient descent algorithm guaranteed to converge?
We can use fixed learning rate during training without worrying about learning rate decay. It has straight trajectory towards the minimum and it is guaranteed to converge in theory to the global minimum if the loss function is convex and to a local minimum if the loss function is not convex. It has unbiased estimate of gradients.